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- v0.0.15 release
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jalammar committed Aug 2, 2021
1 parent 8670f7b commit ab08cac
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Showing 3 changed files with 4 additions and 4 deletions.
2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -25,7 +25,7 @@ def read(*names, **kwargs):

setup(
name='ecco',
version='0.0.14',
version='0.0.15',
license='BSD-3-Clause',
description='Visualization tools for NLP machine learning models.',
long_description='%s\n%s' % (
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2 changes: 1 addition & 1 deletion src/ecco/__init__.py
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Expand Up @@ -12,7 +12,7 @@
"""


__version__ = '0.0.14'
__version__ = '0.0.15'
from ecco.lm import LM
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
from typing import Optional, List
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4 changes: 2 additions & 2 deletions src/ecco/analysis.py
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Expand Up @@ -9,7 +9,7 @@ def cca(acts1, acts2):
average of all the correlation coefficients.
Args:
acts1: Activations matrix #1. 2D numPy array. Dimensions: (neurons, token position)
acts2: Activations matrix #2. 2D numPy array. Dimensions: (neurons, token position)
acts2: Activations matrix #2. 2D numPy array. Dimensions: (neurons, token position)
Returns:
score: Float between 0 and 1, where 0 means not correlated, 1 means the two activation matrices are linear transformations of each other.
Expand Down Expand Up @@ -69,7 +69,7 @@ def pwcca(acts1, acts2, epsilon=1e-10):

def cka(acts1, acts2):
"""
Calculates a similarity score for two activation matrices using center kernal analysis (CKA). CKA is more
Calculates a similarity score for two activation matrices using center kernel alignment (CKA). CKA is more
has the benefit of not requiring the number of tokens to be larger than the number of neurons.
Args:
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